Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations1470
Missing cells1470
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory887.4 B

Variable types

Numeric11
Boolean3
Categorical15
DateTime1
Unsupported1

Alerts

Age is highly overall correlated with TotalWorkingYearsHigh correlation
Department is highly overall correlated with JobRoleHigh correlation
JobLevel is highly overall correlated with JobRole and 2 other fieldsHigh correlation
JobRole is highly overall correlated with Department and 1 other fieldsHigh correlation
MaritalStatus is highly overall correlated with StockOptionLevelHigh correlation
MonthlyIncome is highly overall correlated with JobLevel and 1 other fieldsHigh correlation
PercentSalaryHike is highly overall correlated with PerformanceRatingHigh correlation
PerformanceRating is highly overall correlated with PercentSalaryHikeHigh correlation
StockOptionLevel is highly overall correlated with MaritalStatusHigh correlation
TotalWorkingYears is highly overall correlated with Age and 3 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsAtCompanyHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompanyHigh correlation
Date_of_termination has 1470 (100.0%) missing values Missing
Date_of_termination is an unsupported type, check if it needs cleaning or further analysis Unsupported
NumCompaniesWorked has 197 (13.4%) zeros Zeros
TrainingTimesLastYear has 54 (3.7%) zeros Zeros
YearsAtCompany has 44 (3.0%) zeros Zeros
YearsSinceLastPromotion has 581 (39.5%) zeros Zeros
YearsWithCurrManager has 263 (17.9%) zeros Zeros
Leaves has 243 (16.5%) zeros Zeros

Reproduction

Analysis started2025-03-12 13:39:29.975596
Analysis finished2025-03-12 13:39:39.364031
Duration9.39 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92381
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-12T14:39:39.431350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1353735
Coefficient of variation (CV)0.24741146
Kurtosis-0.40414514
Mean36.92381
Median Absolute Deviation (MAD)6
Skewness0.4132863
Sum54278
Variance83.455049
MonotonicityNot monotonic
2025-03-12T14:39:39.496871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35 78
 
5.3%
34 77
 
5.2%
31 69
 
4.7%
36 69
 
4.7%
29 68
 
4.6%
32 61
 
4.1%
30 60
 
4.1%
33 58
 
3.9%
38 58
 
3.9%
40 57
 
3.9%
Other values (33) 815
55.4%
ValueCountFrequency (%)
18 8
 
0.5%
19 9
 
0.6%
20 11
 
0.7%
21 13
 
0.9%
22 16
 
1.1%
23 14
 
1.0%
24 26
1.8%
25 26
1.8%
26 39
2.7%
27 48
3.3%
ValueCountFrequency (%)
60 5
 
0.3%
59 10
0.7%
58 14
1.0%
57 4
 
0.3%
56 14
1.0%
55 22
1.5%
54 18
1.2%
53 19
1.3%
52 18
1.2%
51 19
1.3%

Attrition
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1233 
True
237 
ValueCountFrequency (%)
False 1233
83.9%
True 237
 
16.1%
2025-03-12T14:39:39.554215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

BusinessTravel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.8 KiB
Travel_Rarely
1043 
Travel_Frequently
277 
Non-Travel
150 

Length

Max length17
Median length13
Mean length13.447619
Min length10

Characters and Unicode

Total characters19768
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Rarely
4th rowTravel_Rarely
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely 1043
71.0%
Travel_Frequently 277
 
18.8%
Non-Travel 150
 
10.2%

Length

2025-03-12T14:39:39.604725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:39.647724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 1043
71.0%
travel_frequently 277
 
18.8%
non-travel 150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
e 3067
15.5%
l 2790
14.1%
r 2790
14.1%
a 2513
12.7%
T 1470
7.4%
v 1470
7.4%
_ 1320
6.7%
y 1320
6.7%
R 1043
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3067
15.5%
l 2790
14.1%
r 2790
14.1%
a 2513
12.7%
T 1470
7.4%
v 1470
7.4%
_ 1320
6.7%
y 1320
6.7%
R 1043
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3067
15.5%
l 2790
14.1%
r 2790
14.1%
a 2513
12.7%
T 1470
7.4%
v 1470
7.4%
_ 1320
6.7%
y 1320
6.7%
R 1043
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3067
15.5%
l 2790
14.1%
r 2790
14.1%
a 2513
12.7%
T 1470
7.4%
v 1470
7.4%
_ 1320
6.7%
y 1320
6.7%
R 1043
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

Department
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size94.2 KiB
Research & Development
961 
Sales
446 
Human Resources
 
63

Length

Max length22
Median length22
Mean length16.542177
Min length5

Characters and Unicode

Total characters24317
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch & Development
2nd rowResearch & Development
3rd rowResearch & Development
4th rowSales
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development 961
65.4%
Sales 446
30.3%
Human Resources 63
 
4.3%

Length

2025-03-12T14:39:39.705239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:39.748237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
research 961
27.8%
961
27.8%
development 961
27.8%
sales 446
12.9%
human 63
 
1.8%
resources 63
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
c 1024
 
4.2%
r 1024
 
4.2%
m 1024
 
4.2%
n 1024
 
4.2%
Other values (10) 7425
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
c 1024
 
4.2%
r 1024
 
4.2%
m 1024
 
4.2%
n 1024
 
4.2%
Other values (10) 7425
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
c 1024
 
4.2%
r 1024
 
4.2%
m 1024
 
4.2%
n 1024
 
4.2%
Other values (10) 7425
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
c 1024
 
4.2%
r 1024
 
4.2%
m 1024
 
4.2%
n 1024
 
4.2%
Other values (10) 7425
30.5%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-12T14:39:39.797717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1068644
Coefficient of variation (CV)0.88189823
Kurtosis-0.2248334
Mean9.192517
Median Absolute Deviation (MAD)5
Skewness0.958118
Sum13513
Variance65.721251
MonotonicityNot monotonic
2025-03-12T14:39:39.855717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 211
14.4%
1 208
14.1%
10 86
 
5.9%
9 85
 
5.8%
3 84
 
5.7%
7 84
 
5.7%
8 80
 
5.4%
5 65
 
4.4%
4 64
 
4.4%
6 59
 
4.0%
Other values (19) 444
30.2%
ValueCountFrequency (%)
1 208
14.1%
2 211
14.4%
3 84
 
5.7%
4 64
 
4.4%
5 65
 
4.4%
6 59
 
4.0%
7 84
 
5.7%
8 80
 
5.4%
9 85
5.8%
10 86
5.9%
ValueCountFrequency (%)
29 27
1.8%
28 23
1.6%
27 12
0.8%
26 25
1.7%
25 25
1.7%
24 28
1.9%
23 27
1.8%
22 19
1.3%
21 18
1.2%
20 25
1.7%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.4 KiB
Male
882 
Female
588 

Length

Max length6
Median length4
Mean length4.8
Min length4

Characters and Unicode

Total characters7056
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 882
60.0%
Female 588
40.0%

Length

2025-03-12T14:39:39.919240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:39.958244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 882
60.0%
female 588
40.0%

Most occurring characters

ValueCountFrequency (%)
e 2058
29.2%
a 1470
20.8%
l 1470
20.8%
M 882
12.5%
F 588
 
8.3%
m 588
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2058
29.2%
a 1470
20.8%
l 1470
20.8%
M 882
12.5%
F 588
 
8.3%
m 588
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2058
29.2%
a 1470
20.8%
l 1470
20.8%
M 882
12.5%
F 588
 
8.3%
m 588
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2058
29.2%
a 1470
20.8%
l 1470
20.8%
M 882
12.5%
F 588
 
8.3%
m 588
 
8.3%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Length

2025-03-12T14:39:40.004759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:40.047756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

JobLevel
Categorical

High correlation 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
1
543 
2
534 
3
218 
4
106 
5
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Length

2025-03-12T14:39:40.101659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:40.146661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

JobRole
Categorical

High correlation 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size96.4 KiB
Sales Executive
326 
Research Scientist
292 
Laboratory Technician
259 
Manufacturing Director
145 
Healthcare Representative
131 
Other values (4)
317 

Length

Max length25
Median length21
Mean length18.070748
Min length7

Characters and Unicode

Total characters26564
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaboratory Technician
2nd rowResearch Scientist
3rd rowResearch Director
4th rowSales Representative
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive 326
22.2%
Research Scientist 292
19.9%
Laboratory Technician 259
17.6%
Manufacturing Director 145
9.9%
Healthcare Representative 131
8.9%
Manager 102
 
6.9%
Sales Representative 83
 
5.6%
Research Director 80
 
5.4%
Human Resources 52
 
3.5%

Length

2025-03-12T14:39:40.207700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:40.267701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sales 409
14.4%
research 372
13.1%
executive 326
11.5%
scientist 292
10.3%
laboratory 259
9.1%
technician 259
9.1%
director 225
7.9%
representative 214
7.5%
manufacturing 145
 
5.1%
healthcare 131
 
4.6%
Other values (3) 206
7.3%

Most occurring characters

ValueCountFrequency (%)
e 3905
14.7%
a 2580
 
9.7%
t 2098
 
7.9%
c 2061
 
7.8%
i 2012
 
7.6%
r 1984
 
7.5%
n 1468
 
5.5%
s 1391
 
5.2%
1368
 
5.1%
o 795
 
3.0%
Other values (19) 6902
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3905
14.7%
a 2580
 
9.7%
t 2098
 
7.9%
c 2061
 
7.8%
i 2012
 
7.6%
r 1984
 
7.5%
n 1468
 
5.5%
s 1391
 
5.2%
1368
 
5.1%
o 795
 
3.0%
Other values (19) 6902
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3905
14.7%
a 2580
 
9.7%
t 2098
 
7.9%
c 2061
 
7.8%
i 2012
 
7.6%
r 1984
 
7.5%
n 1468
 
5.5%
s 1391
 
5.2%
1368
 
5.1%
o 795
 
3.0%
Other values (19) 6902
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3905
14.7%
a 2580
 
9.7%
t 2098
 
7.9%
c 2061
 
7.8%
i 2012
 
7.6%
r 1984
 
7.5%
n 1468
 
5.5%
s 1391
 
5.2%
1368
 
5.1%
o 795
 
3.0%
Other values (19) 6902
26.0%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row1
4th row1
5th row4

Common Values

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Length

2025-03-12T14:39:40.356336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:40.399870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring characters

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

MaritalStatus
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size80.4 KiB
Married
673 
Single
470 
Divorced
327 

Length

Max length8
Median length7
Mean length6.9027211
Min length6

Characters and Unicode

Total characters10147
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowMarried
4th rowDivorced
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 673
45.8%
Single 470
32.0%
Divorced 327
22.2%

Length

2025-03-12T14:39:40.470876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:40.517388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
married 673
45.8%
single 470
32.0%
divorced 327
22.2%

Most occurring characters

ValueCountFrequency (%)
r 1673
16.5%
i 1470
14.5%
e 1470
14.5%
d 1000
9.9%
a 673
6.6%
M 673
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1673
16.5%
i 1470
14.5%
e 1470
14.5%
d 1000
9.9%
a 673
6.6%
M 673
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1673
16.5%
i 1470
14.5%
e 1470
14.5%
d 1000
9.9%
a 673
6.6%
M 673
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1673
16.5%
i 1470
14.5%
e 1470
14.5%
d 1000
9.9%
a 673
6.6%
M 673
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

MonthlyIncome
Real number (ℝ)

High correlation 

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.9313
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-12T14:39:40.574899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.9568
Coefficient of variation (CV)0.72397455
Kurtosis1.0052327
Mean6502.9313
Median Absolute Deviation (MAD)2199
Skewness1.3698167
Sum9559309
Variance22164857
MonotonicityNot monotonic
2025-03-12T14:39:40.652199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2342 4
 
0.3%
2380 3
 
0.2%
3452 3
 
0.2%
6142 3
 
0.2%
5562 3
 
0.2%
2451 3
 
0.2%
2404 3
 
0.2%
2610 3
 
0.2%
2559 3
 
0.2%
6347 3
 
0.2%
Other values (1339) 1439
97.9%
ValueCountFrequency (%)
1009 1
0.1%
1051 1
0.1%
1052 1
0.1%
1081 1
0.1%
1091 1
0.1%
1102 1
0.1%
1118 1
0.1%
1129 1
0.1%
1200 1
0.1%
1223 1
0.1%
ValueCountFrequency (%)
19999 1
0.1%
19973 1
0.1%
19943 1
0.1%
19926 1
0.1%
19859 1
0.1%
19847 1
0.1%
19845 1
0.1%
19833 1
0.1%
19740 1
0.1%
19717 1
0.1%

NumCompaniesWorked
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6931973
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-12T14:39:40.711560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009
Coefficient of variation (CV)0.92752545
Kurtosis0.010213817
Mean2.6931973
Median Absolute Deviation (MAD)1
Skewness1.0264711
Sum3959
Variance6.240049
MonotonicityNot monotonic
2025-03-12T14:39:40.756562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 521
35.4%
0 197
 
13.4%
3 159
 
10.8%
2 146
 
9.9%
4 139
 
9.5%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
9 52
 
3.5%
8 49
 
3.3%
ValueCountFrequency (%)
0 197
 
13.4%
1 521
35.4%
2 146
 
9.9%
3 159
 
10.8%
4 139
 
9.5%
5 63
 
4.3%
6 70
 
4.8%
7 74
 
5.0%
8 49
 
3.3%
9 52
 
3.5%
ValueCountFrequency (%)
9 52
 
3.5%
8 49
 
3.3%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
4 139
 
9.5%
3 159
 
10.8%
2 146
 
9.9%
1 521
35.4%
0 197
 
13.4%

OverTime
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1054 
True
416 
ValueCountFrequency (%)
False 1054
71.7%
True 416
 
28.3%
2025-03-12T14:39:40.794075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.209524
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-12T14:39:40.829077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6599377
Coefficient of variation (CV)0.2406346
Kurtosis-0.30059822
Mean15.209524
Median Absolute Deviation (MAD)2
Skewness0.82112798
Sum22358
Variance13.395144
MonotonicityNot monotonic
2025-03-12T14:39:40.877593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 210
14.3%
13 209
14.2%
14 201
13.7%
12 198
13.5%
15 101
6.9%
18 89
6.1%
17 82
 
5.6%
16 78
 
5.3%
19 76
 
5.2%
22 56
 
3.8%
Other values (5) 170
11.6%
ValueCountFrequency (%)
11 210
14.3%
12 198
13.5%
13 209
14.2%
14 201
13.7%
15 101
6.9%
16 78
 
5.3%
17 82
 
5.6%
18 89
6.1%
19 76
 
5.2%
20 55
 
3.7%
ValueCountFrequency (%)
25 18
 
1.2%
24 21
 
1.4%
23 28
 
1.9%
22 56
3.8%
21 48
3.3%
20 55
3.7%
19 76
5.2%
18 89
6.1%
17 82
5.6%
16 78
5.3%

PerformanceRating
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
3
1244 
4
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Length

2025-03-12T14:39:40.947601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:40.984416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring characters

ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

StockOptionLevel
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
0
631 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Length

2025-03-12T14:39:41.031415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:41.074934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

TotalWorkingYears
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.279592
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-12T14:39:41.129933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7807817
Coefficient of variation (CV)0.68981057
Kurtosis0.91826954
Mean11.279592
Median Absolute Deviation (MAD)4
Skewness1.1171719
Sum16581
Variance60.540563
MonotonicityNot monotonic
2025-03-12T14:39:41.199112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 202
 
13.7%
6 125
 
8.5%
8 103
 
7.0%
9 96
 
6.5%
5 88
 
6.0%
7 81
 
5.5%
1 81
 
5.5%
4 63
 
4.3%
12 48
 
3.3%
3 42
 
2.9%
Other values (30) 541
36.8%
ValueCountFrequency (%)
0 11
 
0.7%
1 81
5.5%
2 31
 
2.1%
3 42
 
2.9%
4 63
4.3%
5 88
6.0%
6 125
8.5%
7 81
5.5%
8 103
7.0%
9 96
6.5%
ValueCountFrequency (%)
40 2
 
0.1%
38 1
 
0.1%
37 4
0.3%
36 6
0.4%
35 3
 
0.2%
34 5
0.3%
33 7
0.5%
32 9
0.6%
31 9
0.6%
30 7
0.5%

TrainingTimesLastYear
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-12T14:39:41.252112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2892706
Coefficient of variation (CV)0.46056569
Kurtosis0.49499299
Mean2.7993197
Median Absolute Deviation (MAD)1
Skewness0.55312417
Sum4115
Variance1.6622187
MonotonicityNot monotonic
2025-03-12T14:39:41.296372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
1 71
 
4.8%
6 65
 
4.4%
0 54
 
3.7%
ValueCountFrequency (%)
0 54
 
3.7%
1 71
 
4.8%
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
6 65
 
4.4%
ValueCountFrequency (%)
6 65
 
4.4%
5 119
 
8.1%
4 123
 
8.4%
3 491
33.4%
2 547
37.2%
1 71
 
4.8%
0 54
 
3.7%

YearsAtCompany
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0081633
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-12T14:39:41.352895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1265252
Coefficient of variation (CV)0.87419841
Kurtosis3.9355088
Mean7.0081633
Median Absolute Deviation (MAD)3
Skewness1.7645295
Sum10302
Variance37.53431
MonotonicityIncreasing
2025-03-12T14:39:41.419918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 196
13.3%
1 171
11.6%
3 128
8.7%
2 127
8.6%
10 120
8.2%
4 110
 
7.5%
7 90
 
6.1%
9 82
 
5.6%
8 80
 
5.4%
6 76
 
5.2%
Other values (27) 290
19.7%
ValueCountFrequency (%)
0 44
 
3.0%
1 171
11.6%
2 127
8.6%
3 128
8.7%
4 110
7.5%
5 196
13.3%
6 76
 
5.2%
7 90
6.1%
8 80
5.4%
9 82
5.6%
ValueCountFrequency (%)
40 1
 
0.1%
37 1
 
0.1%
36 2
 
0.1%
34 1
 
0.1%
33 5
0.3%
32 3
0.2%
31 3
0.2%
30 1
 
0.1%
29 2
 
0.1%
27 2
 
0.1%

YearsSinceLastPromotion
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1877551
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-12T14:39:41.476432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2224303
Coefficient of variation (CV)1.4729392
Kurtosis3.6126731
Mean2.1877551
Median Absolute Deviation (MAD)1
Skewness1.98429
Sum3216
Variance10.384057
MonotonicityNot monotonic
2025-03-12T14:39:41.528976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
7 76
 
5.2%
4 61
 
4.1%
3 52
 
3.5%
5 45
 
3.1%
6 32
 
2.2%
11 24
 
1.6%
8 18
 
1.2%
Other values (6) 65
 
4.4%
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
3 52
 
3.5%
4 61
 
4.1%
5 45
 
3.1%
6 32
 
2.2%
7 76
 
5.2%
8 18
 
1.2%
9 17
 
1.2%
ValueCountFrequency (%)
15 13
 
0.9%
14 9
 
0.6%
13 10
 
0.7%
12 10
 
0.7%
11 24
 
1.6%
10 6
 
0.4%
9 17
 
1.2%
8 18
 
1.2%
7 76
5.2%
6 32
2.2%

YearsWithCurrManager
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1231293
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-12T14:39:41.581647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5681361
Coefficient of variation (CV)0.86539517
Kurtosis0.17105808
Mean4.1231293
Median Absolute Deviation (MAD)3
Skewness0.83345099
Sum6061
Variance12.731595
MonotonicityNot monotonic
2025-03-12T14:39:41.637644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 344
23.4%
0 263
17.9%
7 216
14.7%
3 142
9.7%
8 107
 
7.3%
4 98
 
6.7%
1 76
 
5.2%
9 64
 
4.4%
5 31
 
2.1%
6 29
 
2.0%
Other values (8) 100
 
6.8%
ValueCountFrequency (%)
0 263
17.9%
1 76
 
5.2%
2 344
23.4%
3 142
9.7%
4 98
 
6.7%
5 31
 
2.1%
6 29
 
2.0%
7 216
14.7%
8 107
 
7.3%
9 64
 
4.4%
ValueCountFrequency (%)
17 7
 
0.5%
16 2
 
0.1%
15 5
 
0.3%
14 5
 
0.3%
13 14
 
1.0%
12 18
 
1.2%
11 22
 
1.5%
10 27
 
1.8%
9 64
4.4%
8 107
7.3%

Higher_Education
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size82.2 KiB
Post-Graduation
387 
Graduation
367 
PHD
358 
12th
358 

Length

Max length15
Median length10
Mean length8.1503401
Min length3

Characters and Unicode

Total characters11981
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowPost-Graduation
4th rowPHD
5th rowPHD

Common Values

ValueCountFrequency (%)
Post-Graduation 387
26.3%
Graduation 367
25.0%
PHD 358
24.4%
12th 358
24.4%

Length

2025-03-12T14:39:41.704803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:41.749364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
post-graduation 387
26.3%
graduation 367
25.0%
phd 358
24.4%
12th 358
24.4%

Most occurring characters

ValueCountFrequency (%)
a 1508
12.6%
t 1499
12.5%
o 1141
9.5%
n 754
 
6.3%
G 754
 
6.3%
r 754
 
6.3%
d 754
 
6.3%
i 754
 
6.3%
u 754
 
6.3%
P 745
 
6.2%
Other values (7) 2564
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1508
12.6%
t 1499
12.5%
o 1141
9.5%
n 754
 
6.3%
G 754
 
6.3%
r 754
 
6.3%
d 754
 
6.3%
i 754
 
6.3%
u 754
 
6.3%
P 745
 
6.2%
Other values (7) 2564
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1508
12.6%
t 1499
12.5%
o 1141
9.5%
n 754
 
6.3%
G 754
 
6.3%
r 754
 
6.3%
d 754
 
6.3%
i 754
 
6.3%
u 754
 
6.3%
P 745
 
6.2%
Other values (7) 2564
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1508
12.6%
t 1499
12.5%
o 1141
9.5%
n 754
 
6.3%
G 754
 
6.3%
r 754
 
6.3%
d 754
 
6.3%
i 754
 
6.3%
u 754
 
6.3%
P 745
 
6.2%
Other values (7) 2564
21.4%
Distinct1112
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Minimum1969-06-19 00:00:00
Maximum2021-12-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-12T14:39:41.815882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:41.989028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Date_of_termination
Unsupported

Missing  Rejected  Unsupported 

Missing1470
Missing (%)100.0%
Memory size11.6 KiB

Mode_of_work
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.8 KiB
WFH
768 
OFFICE
702 

Length

Max length6
Median length3
Mean length4.4326531
Min length3

Characters and Unicode

Total characters6516
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOFFICE
2nd rowWFH
3rd rowWFH
4th rowOFFICE
5th rowWFH

Common Values

ValueCountFrequency (%)
WFH 768
52.2%
OFFICE 702
47.8%

Length

2025-03-12T14:39:42.063035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:42.098556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
wfh 768
52.2%
office 702
47.8%

Most occurring characters

ValueCountFrequency (%)
F 2172
33.3%
W 768
 
11.8%
H 768
 
11.8%
O 702
 
10.8%
I 702
 
10.8%
C 702
 
10.8%
E 702
 
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 2172
33.3%
W 768
 
11.8%
H 768
 
11.8%
O 702
 
10.8%
I 702
 
10.8%
C 702
 
10.8%
E 702
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 2172
33.3%
W 768
 
11.8%
H 768
 
11.8%
O 702
 
10.8%
I 702
 
10.8%
C 702
 
10.8%
E 702
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 2172
33.3%
W 768
 
11.8%
H 768
 
11.8%
O 702
 
10.8%
I 702
 
10.8%
C 702
 
10.8%
E 702
 
10.8%

Leaves
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5687075
Minimum0
Maximum5
Zeros243
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-12T14:39:42.132556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7161707
Coefficient of variation (CV)0.66810673
Kurtosis-1.2847046
Mean2.5687075
Median Absolute Deviation (MAD)1
Skewness-0.087394037
Sum3776
Variance2.945242
MonotonicityNot monotonic
2025-03-12T14:39:42.178077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 279
19.0%
3 251
17.1%
5 248
16.9%
0 243
16.5%
1 231
15.7%
2 218
14.8%
ValueCountFrequency (%)
0 243
16.5%
1 231
15.7%
2 218
14.8%
3 251
17.1%
4 279
19.0%
5 248
16.9%
ValueCountFrequency (%)
5 248
16.9%
4 279
19.0%
3 251
17.1%
2 218
14.8%
1 231
15.7%
0 243
16.5%

Absenteeism
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
1
395 
2
373 
3
367 
0
335 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row0
5th row2

Common Values

ValueCountFrequency (%)
1 395
26.9%
2 373
25.4%
3 367
25.0%
0 335
22.8%

Length

2025-03-12T14:39:42.233078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:42.277107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 395
26.9%
2 373
25.4%
3 367
25.0%
0 335
22.8%

Most occurring characters

ValueCountFrequency (%)
1 395
26.9%
2 373
25.4%
3 367
25.0%
0 335
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 395
26.9%
2 373
25.4%
3 367
25.0%
0 335
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 395
26.9%
2 373
25.4%
3 367
25.0%
0 335
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 395
26.9%
2 373
25.4%
3 367
25.0%
0 335
22.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
736 
True
734 
ValueCountFrequency (%)
False 736
50.1%
True 734
49.9%
2025-03-12T14:39:42.313614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Source_of_Hire
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size83.0 KiB
Recruiter
390 
Job Event
372 
Walk-in
361 
Job Portal
347 

Length

Max length10
Median length9
Mean length8.744898
Min length7

Characters and Unicode

Total characters12855
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJob Event
2nd rowRecruiter
3rd rowJob Event
4th rowRecruiter
5th rowJob Event

Common Values

ValueCountFrequency (%)
Recruiter 390
26.5%
Job Event 372
25.3%
Walk-in 361
24.6%
Job Portal 347
23.6%

Length

2025-03-12T14:39:42.360618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:42.405641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
job 719
32.8%
recruiter 390
17.8%
event 372
17.0%
walk-in 361
16.5%
portal 347
15.9%

Most occurring characters

ValueCountFrequency (%)
e 1152
 
9.0%
r 1127
 
8.8%
t 1109
 
8.6%
o 1066
 
8.3%
i 751
 
5.8%
n 733
 
5.7%
719
 
5.6%
b 719
 
5.6%
J 719
 
5.6%
a 708
 
5.5%
Other values (10) 4052
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1152
 
9.0%
r 1127
 
8.8%
t 1109
 
8.6%
o 1066
 
8.3%
i 751
 
5.8%
n 733
 
5.7%
719
 
5.6%
b 719
 
5.6%
J 719
 
5.6%
a 708
 
5.5%
Other values (10) 4052
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1152
 
9.0%
r 1127
 
8.8%
t 1109
 
8.6%
o 1066
 
8.3%
i 751
 
5.8%
n 733
 
5.7%
719
 
5.6%
b 719
 
5.6%
J 719
 
5.6%
a 708
 
5.5%
Other values (10) 4052
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1152
 
9.0%
r 1127
 
8.8%
t 1109
 
8.6%
o 1066
 
8.3%
i 751
 
5.8%
n 733
 
5.7%
719
 
5.6%
b 719
 
5.6%
J 719
 
5.6%
a 708
 
5.5%
Other values (10) 4052
31.5%

Job_mode
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size82.4 KiB
FullTime
517 
Contract
482 
Part Time
471 

Length

Max length9
Median length8
Mean length8.3204082
Min length8

Characters and Unicode

Total characters12231
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowContract
2nd rowPart Time
3rd rowContract
4th rowFullTime
5th rowContract

Common Values

ValueCountFrequency (%)
FullTime 517
35.2%
Contract 482
32.8%
Part Time 471
32.0%

Length

2025-03-12T14:39:42.463645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T14:39:42.505161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fulltime 517
26.6%
contract 482
24.8%
part 471
24.3%
time 471
24.3%

Most occurring characters

ValueCountFrequency (%)
t 1435
11.7%
l 1034
 
8.5%
m 988
 
8.1%
T 988
 
8.1%
e 988
 
8.1%
i 988
 
8.1%
r 953
 
7.8%
a 953
 
7.8%
u 517
 
4.2%
F 517
 
4.2%
Other values (6) 2870
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12231
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1435
11.7%
l 1034
 
8.5%
m 988
 
8.1%
T 988
 
8.1%
e 988
 
8.1%
i 988
 
8.1%
r 953
 
7.8%
a 953
 
7.8%
u 517
 
4.2%
F 517
 
4.2%
Other values (6) 2870
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12231
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1435
11.7%
l 1034
 
8.5%
m 988
 
8.1%
T 988
 
8.1%
e 988
 
8.1%
i 988
 
8.1%
r 953
 
7.8%
a 953
 
7.8%
u 517
 
4.2%
F 517
 
4.2%
Other values (6) 2870
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12231
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1435
11.7%
l 1034
 
8.5%
m 988
 
8.1%
T 988
 
8.1%
e 988
 
8.1%
i 988
 
8.1%
r 953
 
7.8%
a 953
 
7.8%
u 517
 
4.2%
F 517
 
4.2%
Other values (6) 2870
23.5%

Interactions

2025-03-12T14:39:38.275104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:31.419924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.163213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.800969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.483073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.088996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.831361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.516755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.163283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.938552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.597236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.326611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:31.587161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.216722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.857974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.533076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.142210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.887881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.570766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.218720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.995066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.654281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.385642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:31.640159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.273023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.922059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.587595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.292765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.948002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.628867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.281619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.055067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.715106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.446643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:31.696675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.333539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.984134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.644594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.353766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.012518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.692061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.345656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.118589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.779630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.499408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:31.745677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.388565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.042132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.695115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.409796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.072030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.746060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.403587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.175102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.836627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.556412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:31.801322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.446565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.104346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.750109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.467806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.140036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.805084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.464588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.234161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.900791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.621926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:31.858327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.507595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.169238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.807633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.530326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.206322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.869089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.528481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.297186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.966038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.677389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:31.942393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.564370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.231342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.863636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.588845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.266325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.925227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.587998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.356185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.027547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.735390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:31.998421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.625880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.295782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.921964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.651330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.329375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.986762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.652995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.419213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.091063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.791907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.054421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.685260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.359784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.978484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.711849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.393240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.045759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.715511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.478734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.155064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.849912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.110209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:32.747259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:33.423633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.036482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:34.774364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:35.459243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.107278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:36.879552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:37.541730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-12T14:39:38.216585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-12T14:39:42.567168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AbsenteeismAgeAttritionBusinessTravelDepartmentDistanceFromHomeGenderHigher_EducationJobInvolvementJobLevelJobRoleJobSatisfactionJob_modeLeavesMaritalStatusMode_of_workMonthlyIncomeNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingSource_of_HireStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWork_accidentYearsAtCompanyYearsSinceLastPromotionYearsWithCurrManager
Absenteeism1.0000.0260.0000.0120.0330.0410.0100.0490.0000.0000.0000.0000.0260.0000.0000.0130.0270.0000.0000.0030.0000.0000.0000.0330.0000.0000.0000.0000.008
Age0.0261.0000.2130.0410.000-0.0190.0000.0000.0250.2950.1750.0000.0000.0360.1410.0000.4720.3530.0000.0080.0000.0000.0930.6570.0000.0090.2520.1740.195
Attrition0.0000.2131.0000.1230.0770.0670.0090.0000.1320.2160.2310.0990.0440.0730.1730.0000.2170.1070.2430.0000.0000.0000.1980.2080.0790.0000.1730.0270.179
BusinessTravel0.0120.0410.1231.0000.0000.0230.0370.0220.0160.0000.0000.0000.0390.0500.0350.0000.0250.0000.0240.0300.0000.0380.0000.0000.0000.0490.0000.0300.064
Department0.0330.0000.0770.0001.0000.0000.0260.0480.0000.2120.9370.0290.0310.0400.0300.0000.1870.0320.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.000
DistanceFromHome0.041-0.0190.0670.0230.0001.0000.0300.0000.0280.0540.0000.0000.019-0.0050.0000.0410.003-0.0100.0660.0300.0580.0000.015-0.003-0.0250.0000.011-0.0050.004
Gender0.0100.0000.0090.0370.0260.0301.0000.0260.0000.0480.0740.0000.0000.0460.0320.0000.0460.0000.0310.0490.0000.0000.0000.0000.0000.0000.0660.0000.000
Higher_Education0.0490.0000.0000.0220.0480.0000.0261.0000.0000.0000.0530.0000.0320.0000.0480.0470.0000.0570.0000.0610.0000.0000.0000.0000.0360.0000.0220.0000.046
JobInvolvement0.0000.0250.1320.0160.0000.0280.0000.0001.0000.0000.0000.0000.0410.0000.0240.0140.0460.0000.0000.0360.0000.0210.0220.0000.0130.0000.0530.0000.044
JobLevel0.0000.2950.2160.0000.2120.0540.0480.0000.0001.0000.5690.0000.0360.0060.0460.0000.8640.1130.0000.0000.0000.0410.0690.5390.0170.0000.3530.2060.232
JobRole0.0000.1750.2310.0000.9370.0000.0740.0530.0000.5691.0000.0000.0360.0000.0610.0000.4230.0790.0000.0000.0000.0510.0390.2930.0000.0000.1880.1110.118
JobSatisfaction0.0000.0000.0990.0000.0290.0000.0000.0000.0000.0000.0001.0000.0000.0090.0000.0000.0000.0000.0220.0000.0260.0210.0000.0240.0210.0000.0000.0000.000
Job_mode0.0260.0000.0440.0390.0310.0190.0000.0320.0410.0360.0360.0001.0000.0000.0180.0370.0300.0260.0000.0280.0000.0000.0000.0790.0000.0000.0520.0650.015
Leaves0.0000.0360.0730.0500.040-0.0050.0460.0000.0000.0060.0000.0090.0001.0000.0500.0000.0090.0180.000-0.0000.0490.0000.0310.0350.0150.0490.0210.0000.033
MaritalStatus0.0000.1410.1730.0350.0300.0000.0320.0480.0240.0460.0610.0000.0180.0501.0000.0260.0610.0380.0000.0000.0000.0000.5810.0690.0000.0420.0000.0350.000
Mode_of_work0.0130.0000.0000.0000.0000.0410.0000.0470.0140.0000.0000.0000.0370.0000.0261.0000.0000.0000.0000.0000.0000.0770.0140.0200.0000.0000.0000.0000.026
MonthlyIncome0.0270.4720.2170.0250.1870.0030.0460.0000.0460.8640.4230.0000.0300.0090.0610.0001.0000.1900.000-0.0340.0000.0230.0560.710-0.0350.0000.4640.2650.365
NumCompaniesWorked0.0000.3530.1070.0000.032-0.0100.0000.0570.0000.1130.0790.0000.0260.0180.0380.0000.1901.0000.0000.0000.0000.0000.0000.315-0.0470.022-0.171-0.067-0.144
OverTime0.0000.0000.2430.0240.0000.0660.0310.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0990.0000.0180.0110.000
PercentSalaryHike0.0030.0080.0000.0300.0000.0300.0490.0610.0360.0000.0000.0000.028-0.0000.0000.000-0.0340.0000.0001.0000.9970.0000.000-0.026-0.0040.000-0.054-0.055-0.026
PerformanceRating0.0000.0000.0000.0000.0000.0580.0000.0000.0000.0000.0000.0260.0000.0490.0000.0000.0000.0000.0000.9971.0000.0000.0000.0000.0000.0000.0000.0000.030
Source_of_Hire0.0000.0000.0000.0380.0000.0000.0000.0000.0210.0410.0510.0210.0000.0000.0000.0770.0230.0000.0000.0000.0001.0000.0000.0240.0000.0360.0000.0240.032
StockOptionLevel0.0000.0930.1980.0000.0000.0150.0000.0000.0220.0690.0390.0000.0000.0310.5810.0140.0560.0000.0000.0000.0000.0001.0000.0640.0000.0000.0120.0560.030
TotalWorkingYears0.0330.6570.2080.0000.024-0.0030.0000.0000.0000.5390.2930.0240.0790.0350.0690.0200.7100.3150.000-0.0260.0000.0240.0641.000-0.0140.0100.5940.3350.495
TrainingTimesLastYear0.0000.0000.0790.0000.000-0.0250.0000.0360.0130.0170.0000.0210.0000.0150.0000.000-0.035-0.0470.099-0.0040.0000.0000.000-0.0141.0000.0000.0010.010-0.012
Work_accident0.0000.0090.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0420.0000.0000.0220.0000.0000.0000.0360.0000.0100.0001.0000.0570.0000.000
YearsAtCompany0.0000.2520.1730.0000.0000.0110.0660.0220.0530.3530.1880.0000.0520.0210.0000.0000.464-0.1710.018-0.0540.0000.0000.0120.5940.0010.0571.0000.5200.843
YearsSinceLastPromotion0.0000.1740.0270.0300.000-0.0050.0000.0000.0000.2060.1110.0000.0650.0000.0350.0000.265-0.0670.011-0.0550.0000.0240.0560.3350.0100.0000.5201.0000.467
YearsWithCurrManager0.0080.1950.1790.0640.0000.0040.0000.0460.0440.2320.1180.0000.0150.0330.0000.0260.365-0.1440.000-0.0260.0300.0320.0300.495-0.0120.0000.8430.4671.000

Missing values

2025-03-12T14:39:38.959426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-12T14:39:39.119446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeAttritionBusinessTravelDepartmentDistanceFromHomeGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingStockOptionLevelTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsSinceLastPromotionYearsWithCurrManagerHigher_EducationDate_of_HireDate_of_terminationMode_of_workLeavesAbsenteeismWork_accidentSource_of_HireJob_mode
037YesTravel_RarelyResearch & Development2Male21Laboratory Technician3Single20906Yes153073000Graduation21-01-2021NaNOFFICE42NoJob EventContract
121NoTravel_RarelyResearch & Development15Male31Research Scientist4Single12321No143006000Graduation13-03-2021NaNWFH52NoRecruiterPart Time
245NoTravel_RarelyResearch & Development6Male33Research Director1Married132454Yes1430173000Post-Graduation23-01-2021NaNWFH13NoJob EventContract
323NoTravel_RarelySales2Male31Sales Representative1Divorced23223No133133000PHD25-04-2021NaNOFFICE10YesRecruiterFullTime
422NoTravel_RarelyResearch & Development15Female31Laboratory Technician4Single28711No153015000PHD14-06-2021NaNWFH52NoJob EventContract
519YesTravel_RarelySales22Male31Sales Representative3Single16751Yes193002000PHD14-04-2021NaNWFH11YesJob PortalPart Time
619YesTravel_FrequentlySales1Female11Sales Representative1Single23250No214015000PHD12-01-2021NaNWFH22NoWalk-inContract
728YesTravel_RarelyResearch & Development2Male31Laboratory Technician3Single34852No113055000Post-Graduation30-05-2021NaNWFH02NoWalk-inContract
829NoTravel_RarelySales2Male22Sales Executive2Married66442No1932102000Graduation28-02-2021NaNOFFICE52NoWalk-inPart Time
918YesTravel_RarelyResearch & Development3Male31Laboratory Technician3Single14201No133002000PHD06-05-2021NaNWFH52NoWalk-inFullTime
AgeAttritionBusinessTravelDepartmentDistanceFromHomeGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeNumCompaniesWorkedOverTimePercentSalaryHikePerformanceRatingStockOptionLevelTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsSinceLastPromotionYearsWithCurrManagerHigher_EducationDate_of_HireDate_of_terminationMode_of_workLeavesAbsenteeismWork_accidentSource_of_HireJob_mode
146052NoTravel_RarelyResearch & Development1Male25Manager3Married199990No143134533119PHD14-03-1988NaNWFH50NoRecruiterFullTime
146152NoNon-TravelSales2Male25Manager3Single190681Yes1830332331512Graduation12-05-1988NaNWFH43YesJob EventPart Time
146255NoNon-TravelResearch & Development8Male24Healthcare Representative2Divorced135771Yes15313433315012th09-04-1988NaNWFH31NoWalk-inPart Time
146351NoTravel_RarelyHuman Resources5Male34Manager2Divorced140261Yes113133233010Post-Graduation04-04-1988NaNOFFICE41YesJob PortalContract
146453YesTravel_RarelyResearch & Development2Female23Manufacturing Director4Married101690No16313443319Post-Graduation20-06-1988NaNWFH40YesRecruiterContract
146552NoTravel_RarelySales3Male24Manager1Married168561No113034334116Post-Graduation05-06-1987NaNOFFICE32NoJob PortalPart Time
146655NoTravel_RarelyResearch & Development1Male35Manager1Single190450Yes143037236413Post-Graduation20-01-1985NaNWFH11NoWalk-inFullTime
146755NoTravel_RarelySales26Male25Manager4Married195861No214136336213Post-Graduation17-02-1985NaNOFFICE21NoRecruiterPart Time
146858NoTravel_RarelySales10Male34Sales Executive3Single138720No13303813718PHD29-06-1984NaNWFH22YesJob EventPart Time
146958YesTravel_RarelyResearch & Development23Female33Healthcare Representative4Married103121No12314034015612th08-02-1981NaNWFH43YesJob PortalFullTime